Considering a collection of N bosonic modes, we define the quadrature operators (X_{2n-1}=frac{1}{sqrt {2}}(a_{n}+a^{dagger}_{n})) and (X_{2n}=frac{1}{sqrt{2} i}(a_{n}-a^{dagger}_{n})) where (n=1,ldots,N), which correspond to the generalised position and momentum operators of the field, respectively.
Under the slow-mode approximation: <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0012623.e045.PNG" class= inline-graphic"/> to partition the network according to the k slow modes, we define a distance measure between the nodes.
In order to determine the distribution of this operation time when there are i data packets in phase R of the active mode, we define the state when the sensor node is in sleep status as an absorbing state and construct a new Markov process with the following transition rate diagram with absorbing state (Figure 3).
For the visual mode we define an essential join cost function that is calculated after aligning the two segments that are to be joined, by calculating the Euclidean differences between the aligned mouth landmark positions in the frames at both sides of the join.
He et al. investigated nucleosome dynamics relative to chromatin binding by androgen receptor and used H3K4me2 ChIP-Seq to describe nucleosome depletion overlapping transcription factor binding that is similar to the "central" mode of remodeling we define here [ 12].
After mode-centering, we defined gains and losses as log2 ratios ≥0.15 or ≤−0.15 (±6 SD of the middle 75% quantile of data) and amplification and deletion as a ratio ≥0.4 or ≤−0.4 (representing 4 and 94% quantiles), respectively.
Further, we enlightened the DDR in different genetic diseases, and by failure mode analysis we defined molecular defects putatively contributing to carcinogenesis.
In order to compare performance of these protocols in RO mode, in this section, we define the valid handoff latency in RO mode as the gap between 'the starting point of L2 handoff', and 'the time that the MN has bound its new CoA with the CN'.
According to the coding information correlation with the mode maps of encoded frames, we define a set of intra mode predictors (P) for depth map treeblock as follows: P=left{{D}_{mathrm{l}},{D}_{mathrm{u}},{D}_{mathrm{u}mathrm{l}},{D}_{mathrm{u}mathrm{r}},{C}_{mathrm{col}},{C}_{mathrm{l}},{C}_{mathrm{u}},{C}_{mathrm{u}mathrm{l}},{C}_{mathrm{u}mathrm{r}}right} (2).
For the recessive mode, we again define a binary variable, but this time it is 1 only if two risk alleles are present and 0 otherwise.
As in the SLH case we define output modes (beta _{mathrm {out}}(t)) that are algebraically related to the inputs and the internal modes.
